From 0c62be155b23dc0070c146275795fa98a3b2c466 Mon Sep 17 00:00:00 2001 From: efir369999 Date: Wed, 6 May 2026 01:10:10 +0300 Subject: [PATCH] fix: broken markdown refs auto-resolved --- .../github/moltbook-analysis/eval/CLICK_MODEL_FINDINGS.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/Russian/Intelligence/Moltbook/github/moltbook-analysis/eval/CLICK_MODEL_FINDINGS.md b/Russian/Intelligence/Moltbook/github/moltbook-analysis/eval/CLICK_MODEL_FINDINGS.md index 16532db..c52ad77 100644 --- a/Russian/Intelligence/Moltbook/github/moltbook-analysis/eval/CLICK_MODEL_FINDINGS.md +++ b/Russian/Intelligence/Moltbook/github/moltbook-analysis/eval/CLICK_MODEL_FINDINGS.md @@ -114,7 +114,7 @@ This experiment tests whether the attribution problem identified in Contribution The two populations learn **structurally different models**. L2=62.11 across 771 parameters (767 community biases + 3 feature weights + 1 intercept) indicates pervasive disagreement, not isolated outliers. KL=0.946 confirms the predicted probability distributions diverge substantially. -![Core model comparison](eval/figures/click_model_core_comparison.png) +![Core model comparison](figures/click_model_core_comparison.png) --- @@ -137,13 +137,13 @@ The two populations learn **structurally different models**. L2=62.11 across 771 | 75% | 0.5614 | 12.3% | -0.6600 | 19.4% | | 100% | 0.5336 | 16.6% | -1.4604 | 164.2% | -![Contamination curve](eval/figures/click_model_contamination_curve.png) +![Contamination curve](figures/click_model_contamination_curve.png) ### Finding 3.2: Degradation Is Non-Linear The AUC degradation curve is approximately linear up to ~60% contamination, then accelerates. The log-likelihood curve diverges sharply above 80%, reflecting severe miscalibration when the model has very little organic training signal remaining. -![Relative degradation](eval/figures/click_model_relative_degradation.png) +![Relative degradation](figures/click_model_relative_degradation.png) ### Finding 3.3: Even Small Contamination Is Detectable